This project involves the morphological analysis of a selection of writing systems
and harnessing their properties to generate new, artificial glyphs. The goal of
the work is two-fold.

First, we aim to find structural regularities within
writing systems by examining a fixed set of morphological features. These
features are extracted from all glyphs of the selected writing systems. The 
salience of these features is tested by running them through a classifier and
inspecting the resulting confusion matrix.

Second, we intend to create new glyphs that carry resemblance to given existing
writing systems. This has been done by iteratively adding random gaussians to an
initially empty solution.  Simulated annealing was used for the non-linear
optimization process. The energy function for the simulated annealing algorithm
was based on the features of the to-be-approximated, existing writing system,
generated in the first part.

The result shows a strong correlation between perimetric complexity and density,
as well as a significant separation between different writing systems based on
this correlation.  The generated glyphs show that the selected features are not
fully capable of capturing the intricacies of the writing systems, but can in
some cases result in realistic new glyphs.
